The general problem addressed by this application is as follows. A computer system is required to obtain solutions to a very large number of independent problems and then generate a comprehensive result based on the solutions. The individual problems may be of widely-differing complexity and may thus demand widely-differing amounts of computational time to solve. Although solving the problems may depend upon one or more common inputs, the problems are independent in that each may be solved without reference to the solution obtained for any of the others. As used herein, the term “problem module” refers to each of the independent problems.
The specific problem addressed by this application is that of evaluating credit exposure associated with an investment portfolio. In particular, it is frequently necessary for investment portfolio owners to quantify their credit exposure to each counterparty within a given investment portfolio. The portfolio typically comprises a collection of counterparties, with each counterparty obligated to make payments to the portfolio owner and/or perform other duties in accordance with one or more financial instrument transactions, referred to herein as “trades”. The financial instruments underlying each transaction may be, for example, derivative instruments such as call or put options having a value dependent upon some financial asset, commodity index, predefined variable, or a combination thereof. The total credit exposure of the portfolio owner to each counterparty may be represented as a sum of a current exposure and a potential exposure. The current exposure represents the maximum loss that the portfolio owner would incur if the counterparty would currently fail to perform its obligations (i.e., default). The potential exposure represents the maximum loss that would be incurred if the counterparty would default at a future date prior to the maturity of the instrument. Credit exposure determinations by the portfolio owner may be used for, among other things, approving additional transactions with a counterparty based upon a predetermined credit limit for that counterparty and for credit risk valuation.
Because a determination of potential exposure is inherently speculative, statistical models are frequently employed for simulating a portfolio owner's potential exposure to each counterparty within a given portfolio. As a first step, such models typically simulate a large number of market scenarios based upon, among other things, one or more market risk factors affecting values of trades associated with each counterparty. Such risk factors may include, for example, future prices of traded assets, interest rates, and currency exchange rates. Each market scenario may, for example, provide numerical values for such factors at a plurality of time horizons, for example, each month for a specified period of time. Each scenario may also include a probability representing the likelihood of its occurrence.
The time over which the market scenarios are simulated may be selected based upon the time remaining until the maturity of financial instruments underlying the trades. Creation of simulated market scenarios may be implemented, for example, using known Monte Carlo simulation techniques. Upon creation of the simulated market scenarios, each trade associated with a particular counterparty may be priced over the time horizons for each simulated market scenario. Based upon the determined pricings, the portfolio owner's potential exposure to the counterparty with respect to each simulated market scenario may be determined. The potential exposures for all of the simulated market scenarios may then be statistically analyzed in order to determine an overall estimate of portfolio owner's potential exposure to the counterparty. If necessary, the estimated potential exposures of counterparties within a portfolio may be combined to determine an estimate of the potential exposure represented by the entire portfolio.
The process of pricing trades in order to determine an estimate of a portfolio owner's credit exposure to each counterparty within a portfolio is computationally intensive. In particular, large investment portfolios routinely comprise tens of thousands of counterparties, with each counterparty contractually obligated to the portfolio owner under one or more transactions. Where a large number of market scenarios (e.g., 1000-2000) is created, as well as a substantial number of time horizons for the scenarios, the number of pricing computations necessary may easily be on the order of tens of billions. The ability of the portfolio owner to make real-time investment decisions based upon credit exposure is thus generally limited by the amount of time required to perform such pricing computations.
The determination of credit exposure is an example of the general problem cited above. In particular, the problem modules cited for the general problem correspond to the calculation of credit exposure for the individual trades associated with a particular counterparty. The determination of credit exposure falls within the scope of the general problem because the credit exposure for each individual trade may be calculated without reference to the credit exposure for any other trade. Because the complexity of trades typically varies widely in scope, calculating credit exposure for the individual trades generally requires widely-differing amounts of computational time.
Pricing trades for determining a portfolio owner's potential exposure has historically been performed using a multi-threaded process approach. FIG. 1 is a block diagram of a prior art computer system for implementing a multi-threaded process approach. As, shown in FIG. 1, a root script 15 initiates a multi-threaded process 20 on each of a plurality of master servers 25 operating in parallel. The root script 15 may be executed, for example, on a control server 16 in communication with each of the master servers 25. Within each master server 25, each thread 30 of the multi-threaded process 20 is implemented within a corresponding server engine 35 and is operative for pricing a trade 40 for a given counterparty 45 over one or more of the simulated market scenarios. Because the number of threads 30 that may be simultaneously implemented by each master server 25 is limited, a considerable amount of time is typically required for each multi-threaded process 20 to price all trades 40 associated with a given counterparty 45 based on the simulated market scenarios.
The time required for each multi-threaded process 20 to price the trades 40 for a given counterparty 45 is largely determined by the number and complexity of the trades 40. In particular, whereas certain of the trades 40 may be simple and thus priced relatively quickly, other trades 40 may be more complex and require significantly more time for pricing. Each trade 40 may have associated with it an algorithm that is to be run for each of the market scenarios over each of the time horizons. The type of trade 40 determines the complexity of the algorithm and the time required for it to run.
Computer systems utilizing purely multi-threaded processes for pricing counterparty trades 40, such as that of FIG. 1, perform all of the pricing computations for a given counterparty 45 within a single process. Although such systems may complete pricing computations in an acceptable period of time where the trades 40 are relatively non-complex, the presence of complex trades 40 may require an unacceptably long time period in order to compute the pricing computations.